Decidable By Construction: Design-Time Verification for Trustworthy AI

Researchers propose a framework for verifying AI model properties at design time rather than after deployment, using algebraic constraints over finitely generated abelian groups. The approach eliminates computational overhead of post-hoc verification by building trustworthiness into the model architecture from the start.

• →AI model correctness can be verified at design time before training begins, rather than requiring post-deployment validation.
• →The framework uses algebraic structures over finitely generated abelian groups where inference is decidable in polynomial time.
• →The approach combines dimensional type systems, program hypergraphs, and adaptive domain architectures to preserve invariants during training.
• →Current AI reliability approaches impose compounding overhead across deployments, layers, and inference requests.
• →The framework connects Hindley-Milner type inference to universal induction theory through Solomonoff's universal prior.